Habibi vs LangSmith
A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.
Habibi Evaluation | LangSmith Evaluation | |
|---|---|---|
| Tagline | Self-hosted generative engine optimization for tracking brand mentions across ChatGPT, Perplexity, and Gemini | LangChain's eval + observability platform. |
| Category | Evaluation | Evaluation |
| Pricing | Freemium· Opsily Server: €40 | Freemium· Developer: $0 · Plus: $39 · Enterprise: Custom pricing |
| Model | GPT-4o, Perplexity Sonar, Gemini (bring-your-own API keys) | Platform (any LLM) |
| Editorial score | — | 8.7 / 10 |
| Use cases | AI answer engine visibility trackingChatGPT brand mention monitoringPerplexity citation trackingGemini share-of-voice reportingCompetitor GEO benchmarkingPage-level citation mappingClient-facing GEO reporting for agenciesGDPR-compliant AI visibility monitoringPrompt-library A/B testing for content changes | LLM tracingevalsLangChain integration |
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| Website | opsily.com | www.langchain.com |
Pick Habibi if
- ✅ Self-hosted with data kept in a local SQLite database, so prompt libraries and competitor lists never leave your infrastructure
- ✅ Flat server-based pricing (from about $20/month via Opsily) instead of per-seat SaaS fees that typically run $99-$579/month
- ✅ Unlimited team seats at no extra cost, which suits agencies managing many client workspaces
- ✅ Multi-sample runs per prompt smooth out LLM non-determinism and give more trustworthy mention-rate numbers
Pick LangSmith if
- ✅ Tight LangChain integration
- ✅ Strong tracing UX
- ✅ Mature dataset/eval flows
- ✅ Reasonable per-seat pricing